finman-empirical-design
GitHub用于强化财务管理稿件的数据层设计，确保样本构建、变量度量及推断的严谨性与可复现性。适用于样本筛选未文档化、变量定义缺乏依据、面板结构存在前瞻偏差或聚类标准误不合理等场景，旨在提升数据质量而非确立因果。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill finman-empirical-design -g -y
SKILL.md
Frontmatter
{
"name": "finman-empirical-design",
"description": "Use when the sample construction, variable measurement, panel structure, or inference of a Financial Management (FM) manuscript is fragile — before identification can be trusted or exhibits finalized. Hardens the data layer; it does not establish the causal claim (finman-identification) or run robustness (finman-robustness)."
}
Empirical Design (finman-empirical-design)
When to trigger
- The sample comes from CRSP / Compustat / a vendor feed and the screens and survivorship choices are not documented
- A key variable (leverage, payout, governance index, a return measure) has several definitions and you picked one without justification
- The panel mixes frequencies, has look-ahead bias, or merges datasets on a fragile key
- Standard errors are reported but the clustering and cross-sectional/time dependence are not justified
The FM empirical-design bar
FM publishes empirical finance across corporate, asset-pricing, and banking data, so the design layer is judged on whether a competent referee could reconstruct your sample and trust your measures. The journal's "less weight on trivial robustness" stance is a double-edged sword: it means you should not bury the paper in redundant checks, but it raises the premium on getting the primary design right the first time — the screens, the variable definitions, the merge, and the inference. FM referees in corporate finance are especially alert to silent sample screens, point-in-time vs. restated accounting data, and clustering that ignores the panel's dependence structure.
The data-layer audit
| Layer | What FM referees check | Common failure |
|---|---|---|
| Sample frame | universe, date range, every screen stated with counts dropped | "standard filters" with no attrition table |
| Survivorship / look-ahead | delisted firms retained; accounting data point-in-time | using restated Compustat as if known contemporaneously |
| Variable construction | each key variable defined, winsorization level stated, source field named | a leverage measure that silently switches book/market |
| Merge integrity | join keys, match rate, unmatched-firm bias | CRSP-Compustat merge with an unreported low match rate |
| Panel structure | frequency, balanced vs. unbalanced, entry/exit handling | mixing annual and quarterly without stating it |
| Inference | clustering level justified by the dependence; few-cluster / two-way addressed | white SEs on a firm-year panel with serial correlation |
Hardening sequence
- Build the attrition table. Start from the raw universe and report the count dropped at each screen; this single exhibit answers most sample-construction doubts.
- Pin every key variable to a source field and a definition. State winsorization (typically 1%/99%) and why; if a variable has competing definitions, justify yours and note the alternative goes to the appendix.
- Defend point-in-time discipline. For accounting variables, use data as it would have been known; for returns, avoid look-ahead in signal construction.
- Justify the clustering. Cluster at the level where the shocks are correlated (firm, industry, state); use two-way (firm and time) when both dimensions have common shocks; address few-cluster with wild-cluster bootstrap.
- Report power/economic scale. State N, the dependent-variable mean, and the standard-deviation-scaled effect so a reader sees the magnitude in context.
Execution bridge (StatsPAI / Stata MCP)
Run the asset-pricing battery, don't just specify it. Full map:
execution-with-mcp. Financial Management is empirical corporate finance + asset pricing; corporate-causal chain (DiD/IV/RDD) plus the factor-zoo haircut for cross-sectional pricing.
- Factor regressions / time-series alphas:
feolswith the right SEs (Newey–West / clustered) — read the alpha and t off the return. - Factor-zoo haircut: after disclosing how many signals were screened, apply
romano_wolf/benjamini_hochbergand report the alpha that survives. - Fama–MacBeth + Shanken EIV are Stata-canonical — run via
mcp__stata-mcp__stata_dowith the vendoredresources/code/(asreg/xtfmb). - Exhibits:
etable; hand formatting to the tables/figures skill.
Report the economic magnitude (bps/month alpha, Sharpe gain); full factor grid → appendix. JF execution walkthrough.
Checklist
- Attrition table from raw universe to estimation sample, with counts per screen
- Every key variable defined, winsorization stated, source field named
- Survivorship and look-ahead bias addressed (point-in-time accounting; clean signals)
- Merge keys and match rate reported; unmatched-firm bias discussed
- Panel frequency and balanced/unbalanced status stated; entry/exit handled
- Clustering level justified; two-way / few-cluster handled where needed
- Dependent-variable mean and N reported so magnitudes are interpretable
Anti-patterns
- "We apply standard filters" with no attrition table or counts
- Restated accounting data used as if it were known at the time (look-ahead)
- A leverage / payout / governance measure that silently switches definition across tables
- CRSP-Compustat (or vendor) merges with an unreported or low match rate
- White / homoskedastic SEs on a firm-year panel with obvious serial and cross-sectional dependence
- Reporting only t-statistics with no dependent-variable mean to anchor the magnitude
Worked vignette (illustrative)
A draft studies payout on a "standard Compustat sample" with white standard errors. A referee cannot reconstruct it. The FM fix: add Table 1 Panel A as an attrition table (raw universe → drop financials/utilities → drop missing payout → final N), define payout precisely as dividends-plus-repurchases over assets winsorized at 1%/99%, switch to standard errors clustered by firm and year (the panel has both firm persistence and common market shocks), and report the dependent-variable mean so the coefficient's economic size is legible. The design is now reconstructable and the inference defensible.
Data-source notes specific to finance
- Compustat: beware restated data — use point-in-time (PIT) snapshots for accounting variables that signals are built from; flag any look-ahead in the merge.
- CRSP: retain delisted securities and apply delisting returns; survivorship bias from dropping them inflates many results.
- CRSP–Compustat link: report the link table used and the match rate; unmatched firms skew toward small/young/foreign issuers.
- Vendor / hand-collected data (governance, syndicated loans, microstructure): describe coverage, the time window, and any sample selection the vendor's universe imposes — FM referees ask "what is not in this dataset?"
- Returns: state whether returns are gross or net, the holding-period convention, and how microcaps/penny stocks are treated.
Referee pushback mapped to the design fix
- "I can't reproduce your sample." → Add the attrition table from the raw universe with counts dropped per screen.
- "Your accounting variable uses restated data." → Switch to point-in-time data and say so in the note.
- "The standard errors look too small." → Justify and report two-way (or wild-cluster) standard errors matched to the panel's dependence.
- "Is this effect economically meaningful?" → Report the dependent-variable mean and a one-SD-scaled effect.
When the design choice is itself the contribution
Some FM papers earn their place through a measurement or sample-construction innovation — a cleaner proxy, a newly merged dataset, a hand-collected sample. When that is the contribution:
- Validate the new measure against an external benchmark or a known case, not just internal consistency.
- Show what it captures that prior proxies miss, with a side-by-side comparison.
- Document construction in painstaking detail in the internet appendix, because the measure is the asset and referees will probe it.
- Connect the measurement gain to a substantive finding — a better proxy is interesting at FM only if it changes what we conclude about a decision-relevant question.
Output format
【Sample frame】universe + date range + screens (attrition table? [Y/N])
【Key variables】defined + winsorized + source fields named? [Y/N]
【Bias controls】survivorship / look-ahead handled? [Y/N]
【Merge】keys + match rate reported? [Y/N]
【Inference】clustering level justified; two-way/few-cluster handled? [Y/N]
【Magnitude】dep-var mean + N reported? [Y/N]
【Next skill】finman-robustness
Version History
- 1839142 Current 2026-07-05 13:14
